Several existing techniques focus on generalizing ideas from adversarial robustness to reason about NN fairness [1]}, [2]}. By viewing unfairness as an adversarial noise that can flip the output of a classifier, these techniques can certify the fairness of a NN locally, i.e., in the neighborhood of a given individual input. In contrast, this paper focuses on global fairness properties where the goal is to ensure that the NN is fair with respect to all the similar inputs in its domain. Such a fundamental difference precludes the use of existing techniques from the literature on adversarial robustness and calls for novel techniques that can provide provable fairness guarantees.
